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      Socioeconomic inequalities of outpatient and inpatient service utilization in China: personal and regional perspectives

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          Abstract

          Background

          China’s health system has shown remarkable progress in health provision and health outcomes in recent decades, however inequality in health care utilization persists and poses a serious social problem. While government pro-poor health policies addressed affordability as the major obstacle to equality in health care access, this policy direction deserves further examination. Our study examines the issue of health care inequalities in China, analyzing both regional and individual socioeconomic factors associated with the inequality, and provides evidence to improve governmental health policies.

          Methods

          The China Health and Nutrition Survey (CHNS) 1991–2011 data were used to analyze the inequality of health care utilization. The random effects logistic regression technique was used to model health care utilization as the dependent variable, and income and regional location as the independent variables, controlling for individuals’ age, gender, marital status, education, health insurance, body mass index (BMI), and period variations. The dynamic trend of 1991–2011 regional disparities was estimated using an interaction term between the regional group dummy and the wave dummy.

          Results

          The probability of using outpatient service and inpatient services during the previous 4 weeks was 8.6 and 1.1% respectively. Compared to urban residents, suburban (OR: 0.802, 95% CI: 0.720–0.893), town (OR: 0.722, 95% CI: 0.648–0.804), rich (OR: 0.728, 95% CI: 0.656–0.807) and poor village (OR: 0.778, 95% CI: 0.698–0.868) residents were less likely to use outpatient service; and rich (OR: 0.609, 95% CI: 0.472–0.785) and poor village (OR: 0.752, 95% CI: 0. 576–0.983) residents were less likely to use inpatient health care. But the differences between income groups were not significant, except the differences between top and bottom income group in outpatient service use.

          Conclusion

          Regional location was a more important factor than individual characteristics in determining access to health care. Besides demand-side subsidies, Chinese policy makers should pay enhanced attention to health care resource allocation to address inequity in health care access.

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          Most cited references31

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          Outcomes associated with matching patients' treatment preferences to physicians' recommendations: study methodology

          Background Patients often express strong preferences for the forms of treatment available for their disease. Incorporating these preferences into the process of treatment decision-making might improve patients' adherence to treatment, contributing to better outcomes. We describe the methodology used in a study aiming to assess treatment outcomes when patients' preferences for treatment are closely matched to recommended treatments. Method Participants included patients with moderate and severe psoriasis attending outpatient dermatology clinics at the University Medical Centre Mannheim, University of Heidelberg, Germany. A self-administered online survey used conjoint analysis to measure participants' preferences for psoriasis treatment options at the initial study visit. Physicians' treatment recommendations were abstracted from each participant's medical records. The Preference Matching Index (PMI), a measure of concordance between the participant's preferences for treatment and the physician's recommended treatment, was determined for each participant at t1 (initial study visit). A clinical outcome measure, the Psoriasis Area and Severity Index, and two participant-derived outcomes assessing treatment satisfaction and health related quality of life were employed at t1, t2 (twelve weeks post-t1) and t3 (twelve weeks post-t2). Change in outcomes was assessed using repeated measures analysis of variance. The association between participants' PMI scores at t1 and outcomes at t2 and t3 was evaluated using multivariate regressions analysis. Discussion We describe methods for capturing concordance between patients' treatment preferences and recommended treatment and for assessing its association with specific treatment outcomes. The methods are intended to promote the incorporation of patients' preferences in treatment decision-making, enhance treatment satisfaction, and improve treatment effectiveness through greater adherence.
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            Self-rated health and mortality in the NHANES-I Epidemiologic Follow-up Study.

            The ability of self-rated health status to predict mortality was tested with data from the National Health and Nutrition Examination Survey (NHANES-I) Epidemiologic Follow-Up Study (NHEFS), conducted from 1971-84. The sample consists of adult NHANES-I respondents ages 25-74 years (N = 6,440) for whom data from a comprehensive physical examination at the initial interview and survival status at follow-up are available. Self-rated health consists of the response to the single item, "Would you say your health in general is excellent, very good, good, fair, or poor?" Proportional hazards analyses indicated that, net of its association with medical diagnoses given in the physical examination, demographic factors, and health related behaviors, self-rated health at Time 1 is associated with mortality over the 12-year follow-up period among middle-aged males, but not among elderly males or females of any age.
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              Assessing health system performance in developing countries: a review of the literature.

              With the setting of ambitious international health goals and an influx of additional development assistance for health, there is growing interest in assessing the performance of health systems in developing countries. This paper proposes a framework for the assessment of health system performance and reviews the literature on indicators currently in use to measure performance using online medical and public health databases. This was complemented by a review of relevant books and reports in the grey literature. The indicators were organized into three categories: effectiveness, equity, and efficiency. Measures of health system effectiveness were improvement in health status, access to and quality of care and, increasingly, patient satisfaction. Measures of equity included access and quality of care for disadvantaged groups together with fair financing, risk protection and accountability. Measures of efficiency were appropriate levels of funding, the cost-effectiveness of interventions, and effective administration. This framework and review of indicators may be helpful to health policy makers interested in assessing the effects of different policies, expenditures, and organizational structures on health outputs and outcomes in developing countries.
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                Author and article information

                Contributors
                zhu_dawei@163.com
                guonasun@126.com
                wangjiannan@sdu.edu.cn
                stephen.nicholas@newcastle.edu.au
                (+1) 706-414-7239 , lichen1@augusta.edu
                Journal
                Int J Equity Health
                Int J Equity Health
                International Journal for Equity in Health
                BioMed Central (London )
                1475-9276
                4 December 2017
                4 December 2017
                2017
                : 16
                : 210
                Affiliations
                [1 ]ISNI 0000 0000 9889 6335, GRID grid.413106.1, Center for Health Policy and Management, Institute of Medical Information & Library, Chinese Academy of Medical Sciences & Peking Union Medical College, ; Beijing, 100020 China
                [2 ]GRID grid.464237.0, China Population and Development Research Center, ; Beijing, 100081 China
                [3 ]ISNI 0000 0004 1761 1174, GRID grid.27255.37, School of Public Health, , Shandong University, ; Jinan, 265400 China
                [4 ]ISNI 0000 0001 0193 3951, GRID grid.412735.6, School of Management and School of Economics, , Tianjin Normal University, ; Tianjin, 300074 China
                [5 ]ISNI 0000 0001 2301 6433, GRID grid.440718.e, Guangdong Research Institute of International Strategies, , Guangdong University of Foreign Studies, ; Guangzhou, 510420 China
                [6 ]GRID grid.443245.0, Beijing Foreign Studies University, ; Beijing, 100089 China
                [7 ]ISNI 0000 0000 8831 109X, GRID grid.266842.c, Newcastle Business School, , University of Newcastle, ; Newcastle, 2308 NSW Australia
                [8 ]ISNI 0000 0001 2284 9329, GRID grid.410427.4, Georgia Prevention Institute, Department of Population Health Sciences, Medical College of Georgia, , Augusta University, ; Augusta, 30912 GA USA
                Article
                706
                10.1186/s12939-017-0706-8
                5715559
                29202843
                ec03bc6a-229b-4e6e-861c-e8b3ed50d7e7
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 13 August 2017
                : 23 November 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 71503267
                Award ID: 71702131
                Funded by: Ministry of Education in China Project of Humanities and Social Sciences
                Award ID: 15YJC630009
                Categories
                Research
                Custom metadata
                © The Author(s) 2017

                Health & Social care
                health care utilization,inequality,resource allocation,health policy
                Health & Social care
                health care utilization, inequality, resource allocation, health policy

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